Variations in parametrial white adipose tissue mass during the mouse estrous cycle: relationship with the expression of peroxisome proliferator-activated receptor-γ and retinoic acid receptor-α
Bibliographic record
Abstract
Estrogen and progestin participate in the regulation of adipose tissue metabolism, and peroxisome proliferator-activated receptor-gamma (PPARgamma) and retinoic acid receptor-alpha (RXRalpha) are absolutely required for adipose tissue development. The present study is to investigate the changes in parametrial fat mass and expression of PPARgamma and RXRalpha during estrous cycle in mice. Parametrial white adipose tissues (WAT), inter-scapula brown adipose tissues, and uteri from female mice were weighed. Blood samples were collected for the measurement of 17 beta-estradiol and progesterone levels. An RNase protection assay and Western blot analysis were used to compare the expression of PPARgamma and RXRalpha in adipose tissue. The mass of parametrial WAT in diestrus was significantly higher compared with estrus. However, there is no significant difference on the mass of brown adipose tissues during estrous cycle. The expression of PPARgamma in WAT in diestrus was significantly higher than that in estrus. The expression of RXRalpha during estrous cycle was unchanged in both white and brown adipose tissues. In conclusion, the variation in parametrial WAT mass during the mouse estrous cycle correlates with changes in the expression of PPARgamma in WAT.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".